In this study, the concentration of eight dissolved heavy metals (Ti, Cr, Mn, Fe, Ni, Mo, Sb, and Ba) in 42 water samples from the Jiulongjiang River, southeast China, were determined by inductively coupled plasma mass spectrometry (ICP-MS). Multivariate statistical methods, including correlation analysis (CA) and factor and principal component analysis (FA/PCA), were analyzed to identify the sources of the elements. Water quality index (WQI) and health risk assessment, including hazard quotient (HQ) and hazard index (HI), were used to evaluate water quality and the impacts on human health. Our results were compared with the drinking water guidelines reported by China, the World Health Organization (WHO), and the United States Environmental Protection Agency (US EPA), revealing that Ti, Mn, and Sb were not within approved limits at some sites and might be the main pollutants in the drainage basin. Based on the spatial distributions, Ti, Mn, Fe, Ni, and Mo showed good similarity, indicating that they might come from similar sources along the river. The CA results also showed that Ti, Mn, Fe, Ni, and Mo had a high correlation coefficient. The FA/PCA results identified three principal components (PC) that accounted for 79.46% of the total variance. PC 1 suggested that a mixed lithogenic and urban land source contributed to Ti, Mn, Fe, Ni, and Mo; PC 2 showed that Cr, Ni, and Mo were influenced by the discharge of industrial effluents; Sb had a strong loading on PC 3, which was controlled by mining activities. The results of the WQI indicated that the water in the Jiulongjiang River was basically categorized as excellent water, but the water quality levels in site W5 and N4 were poorer due to urban land use. Hazard quotient and HI values showed that Sb was a potential threat to human health, indicating that preventive actions should be considered in regard to mining activities in the upper reaches of Beixi stream.
Keywords: correlation analysis; dissolved heavy metals; factor and principal component analysis; health risk assessment; the Jiulongjiang River; water quality index.